AI Energy Edge Orchestration for Hybrid Grid Coordination
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Solution Overview
Problem
The energy market is transitioning from a centralized model to a decentralized one, requiring a platform that manages and improves legacy infrastructure while coordinating with distributed systems, leveraging AI and IoT technologies for efficient energy management and transaction enablement.
Innovation Solution
An AI-based energy edge platform that integrates with distributed energy resources (DERs) and IoT devices to optimize energy generation, storage, and consumption, utilizing intelligent data layers, smart contracts, digital twins, and energy orchestration systems for autonomous transaction execution and energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized energy management model is used, then infrastructure control is simplified, but the system lacks adaptability to distributed energy systems
Solution Approach 1:
The energy management system is segmented into multiple layers: a centralized cloud platform for high-level coordination and distributed edge devices for local energy management. This segmentation allows the system to handle both centralized control needs and distributed energy system requirements simultaneously, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
An AI-based energy edge platform acts as an intermediary between legacy centralized infrastructure and new distributed energy systems. This intermediary layer translates and coordinates between different system architectures, enabling adaptability without requiring complete system replacement or excessive complexity.
2Productivity
If legacy infrastructure is maintained as-is, then existing systems continue to operate, but efficiency and coordination with new systems are limited
Solution Approach 1:
The platform implements dynamic adaptability where AI algorithms continuously learn from energy data and adjust management strategies in real-time. This dynamic approach allows the system to optimize energy management efficiency while accommodating both legacy infrastructure constraints and new distributed system capabilities without requiring complete system redesign.
Solution Approach 2:
The system changes operational parameters dynamically based on real-time conditions, energy sources available, and system state. By adjusting parameters rather than restructuring the entire system, the platform improves energy management efficiency while maintaining ease of integration with existing legacy infrastructure.
3Adaptability or versatility
If distributed energy systems are integrated, then system adaptability and localization improve, but coordination complexity increases
Solution Approach 1:
The energy edge platform is designed with universal functionality to work with multiple types of energy systems simultaneously - both legacy centralized infrastructure and new distributed energy resources. This multi-functionality enables coordinated management of hybrid systems without proportionally increasing platform complexity, as the same core AI algorithms handle diverse system types.
Data Source
AI summary
Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a set of edge devices is configured to communicate with at least one energy generation facility, energy storage facility, and/or energy consumption system and automatically execute a set of preconfigured policies that govern energy generation, energy storage, or energy consumption of the respective energy generation facilities, energy storage facilities, or energy consumption systems. In some embodiments, the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generation entities in an energy grid.